The impact of platelet-to-lymphocyte ratio on clinical outcomes in heart failure: a systematic review and meta-analysis

Mehrbod Vakhshoori; Niloofar Bondariyan; Sadeq Sabouhi; Keivan Kiani; Nazanin Alaei Faradonbeh; Sayed Ali Emami; Mehrnaz Shakarami; Farbod Khanizadeh; Shahin Sanaei; Niloofaralsadat Motamedi; Davood Shafie

doi:10.1177/17539447241227287

. 2024 Feb 2;18:17539447241227287. doi: 10.1177/17539447241227287

The impact of platelet-to-lymphocyte ratio on clinical outcomes in heart failure: a systematic review and meta-analysis

Mehrbod Vakhshoori ^1,^*,^✉, Niloofar Bondariyan ^2,^*, Sadeq Sabouhi ^3,^*, Keivan Kiani ⁴, Nazanin Alaei Faradonbeh ⁵, Sayed Ali Emami ⁶, Mehrnaz Shakarami ⁷, Farbod Khanizadeh ⁸, Shahin Sanaei ⁹, Niloofaralsadat Motamedi ¹⁰, Davood Shafie ¹¹

¹Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Hezar Jarib Avenue, Isfahan, Iran

²Department of Clinical Pharmacy, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran

³Student Research Committee, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

⁴Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran

⁵Department of Emergency Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

⁶Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran

⁷Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran

⁸Insurance Research Center, Tehran, Iran

⁹Department of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

¹⁰Department of Pediatrics, Arak University of Medical Sciences, Arak, Iran

¹¹Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran

^✉

Email: mehrbod10@yahoo.com

These authors contributed equally and are considered to be co-first authors.

Roles

Mehrbod Vakhshoori: Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Niloofar Bondariyan: Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Sadeq Sabouhi: Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing

Keivan Kiani: Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Nazanin Alaei Faradonbeh: Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Sayed Ali Emami: Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing

Mehrnaz Shakarami: Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Farbod Khanizadeh: Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Shahin Sanaei: Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Niloofaralsadat Motamedi: Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Davood Shafie: Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing

PMCID: PMC10838041 PMID: 38305256

Abstract

Background:

Inflammation has been suggested to play a role in heart failure (HF) pathogenesis. However, the role of platelet-to-lymphocyte ratio (PLR), as a novel biomarker, to assess HF prognosis needs to be investigated. We sought to evaluate the impact of PLR on HF clinical outcomes.

Methods:

English-published records in PubMed/Medline, Scopus, and Web-of-science databases were screened until December 2023. Relevant articles evaluated PLR with clinical outcomes (including mortality, rehospitalization, HF worsening, and HF detection) were recruited, with PLR difference analysis based on death/survival status in total and HF with reduced ejection fraction (HFrEF) patients.

Results:

In total, 21 articles (n = 13,924) were selected. The total mean age was 70.36 ± 12.88 years (males: 61.72%). Mean PLR was 165.54 [95% confidence interval (CI): 154.69–176.38]. In total, 18 articles (n = 10,084) reported mortality [either follow-up (PLR: 162.55, 95% CI: 149.35–175.75) or in-hospital (PLR: 192.83, 95% CI: 150.06–235.61) death rate] and the mean PLR was 166.68 (95% CI: 154.87–178.50). Further analysis revealed PLR was significantly lower in survived HF patients rather than deceased group (152.34, 95% CI: 134.01–170.68 versus 194.73, 95% CI: 175.60–213.85, standard mean difference: −0.592, 95% CI: −0.857 to −0.326, p < 0.001). A similar trend was observed for HFrEF patients. PLR failed to show any association with mortality risk (hazard ratio: 1.02, 95% CI: 0.99–1.05, p = 0.289). Analysis of other aforementioned outcomes was not possible due to the presence of few studies of interest.

Conclusion:

PLR should be used with caution for prognosis assessment in HF sufferers and other studies are necessary to explore the exact association.

Keywords: heart failure, meta-analysis, mortality, platelet-to-lymphocyte ratio, prognosis, systematic review

Plain language summary

Platelet to lymphocyte ratio and heart failure

Inflammation plays a role in heart failure (HF), and a blood test called the platelet-to-lymphocyte ratio (PLR) might be helpful in predicting patients’ outcomes. We found that deceased HF patients had higher PLR values in comparison to those who survived, irrespective of cardiac pump function, with similar pattern for patients with decreased cardiac function (HF with reduced ejection fraction). However, this biomarker failed to show any significant association with death risk. In conclusion, PLR may have some potential to help predict HF prognosis, but it needs more research and physicians should probably be cautious about using PLR alone in clinical settings.

Introduction

Heart failure (HF) is officially defined as ‘a complex clinical syndrome that results from any structural or functional impairment of ventricular filling or ejection of blood. The cardinal manifestations of HF are dyspnea and fatigue, which may limit exercise tolerance, and fluid retention, which may lead to pulmonary and/or splanchnic congestion and/or peripheral edema’.¹ Its prevalence ranges from 0.2% to 17.7%.² More than 5.5 million individuals suffer from HF in the United States and this number rises to 23 million patients around the globe.³ Elderly people are more susceptible to HF in a way that about 10% of people aged at least 70 years experienced HF.⁴ Despite recent progress in HF diagnosis and introduction of some novel therapies, this disease is not well controlled.^5,6 Mortality is another dilemma in HF and it has been reported to be 50–75% in the first 5 years after HF diagnosis.^7–9 One-year HF case fatality rate has been reported from 4% to 45%.² Moreover, HF economic burden should also be considered. Current reports indicate that 1–3% of overall health expenditure is attributed to HF management, mostly related to repetitive hospitalizations as well as extended length of hospital stay.¹⁰ The annual economic cost of HF was reported to be approximately $108 billion, necessitating early assessment to decrease unfavorable outcomes.¹¹ Till now, several HF prognostic factors have been suggested including shock index, pro-brain natriuretic peptide, blood pressure, age, and hemoglobin.^12–16

Inflammation, thrombosis, and inflammatory markers have been recently reported to play a role in pathogenesis cardiovascular diseases (CVDs), especially HF.^17–21 Platelets as well as leukocytes are major inflammatory mediators and the division of platelets to lymphocytes results in introduction of a new inflammatory index, named platelet-to-lymphocyte ratio (PLR).²² This index has been reported to have practical utility in a wide spectrum of disorders like renal disorders, CVDs, and malignancies.^23–25 Although this simple tool has been used to assess prognosis in HF sufferers, reported outcomes are still controversial in a way that some records were in favor of its usefulness and the others failed to find any prognostic capability.^26–28

With respect to these inconsistent results, we sought to implement this systematic review and meta-analysis to assess probable impact of PLR on clinical outcomes among individuals with HF.

Materials and methods

Protocol registration

This systematic review and meta-analysis was performed in context of Preferred Reporting Items for Systematic Reviews and Meta-Analyses.²⁹ We also registered this study in the International Prospective Register of Systematic Reviews (PROSPERO) electronic database (CRD42022335383).

Eligibility criteria

All English-published peer-reviewed cross-sectional, cohort, case control, systematic reviews, and randomized clinical trial (RCT) studies reported the PLR effect with clinical outcomes in HF subjects were recruited. In addition to animal studies, case report and case series studies as well as letters without providing the desired output or with incomplete data were excluded.

Search strategy

We searched PubMed/Medline, Scopus, and Web of science electronic databases without any time limitation. All fields were searched in PubMed/Medline and Web of science databases. In Scopus, title, abstract, and keywords were investigated. We used the following search strategy in all aforementioned databases till December 2023: (‘heart failure’ OR ‘cardiac failure’ OR ‘heart insufficiency’ OR ‘cardiac insufficiency’ OR ‘congestive heart failure’ OR ‘congestive cardiac failure’ OR ‘decompensated heart failure’ OR ‘decompensated cardiac failure’ OR ‘decompensated heart insufficiency’ OR ‘decompensated cardiac insufficiency’ OR ‘acute decompensated heart failure’ OR ‘acute decompensated cardiac failure’ OR ‘acute decompensated heart insufficiency’ OR ‘acute decompensated cardiac insufficiency’ OR ‘hf’) AND (‘platelet^★ to lymphocyte^★ ratio’ OR ‘platelet^★-lymphocyte^★’ OR ‘platelet^★-lymphocyte^★ ratio’ OR ‘platelet^★ to lymphocyte^★’ OR ‘platelet^★-to-lymphocyte^★ ratio’ OR ‘platelet^★-to lymphocyte^★ ratio’ OR ‘platelet^★ to-lymphocyte^★ ratio’ OR ‘platelet^★/lymphocyte^★ ratio’ OR ‘platelet^★/lymphocyte^★’ OR ‘plt/lymph’ OR ‘plt to lymph^{^★} ratio’ OR ‘plt to lymph ratio’ OR ‘plr’).

Data management and selection process

Titles and abstracts were carefully screened by two independent reviewers and all relevant records with their full texts were gathered. In case of any duplicated articles, only single record was considered. Figure 1 illustrates flow diagram of current study.

Figure 1. — Flow diagram of the study.

PLR, platelet-to-lymphocyte ratio.

Data collection process

In each record, the following items were extracted: first author’s name, publication year, study design, sample size, male gander, age [mean ± standard deviation (SD) or median (interquartile range (IQR)), as reported], follow-up duration (as applicable), PLR [mean ± SD, median (IQR), range, as reported], PLR quartiles, tertiles and cutoff values (as reported), and outcomes (follow-up death, in-hospital mortality, rehospitalization, worsening of HF, and HF detection, as reported). Any disagreement was resolved by consensus.

Risk of bias and quality assessment

The quality of cross-sectional studies was assessed through a critical appraisal tool (AXIS).³⁰ Case–control and cohort studies were evaluated through National Institute of Health quality assessment tool and Joanna Briggs Institute (JBI) critical appraisal checklist for cohort studies, respectively (Study Quality Assessment Tools and The Joanna Briggs Institute Critical Appraisal tools for use in JBI Systematic Reviews Checklist for Cohort Studies). Assessment of multiple systematic reviews and JBI critical appraisal checklists for RCT were used to assess risk of bias for systematic reviews and RCTs, respectively (The Joanna Briggs Institute Critical Appraisal tools for use in JBI Systematic Reviews Checklist and for Randomized Controlled Trials).³¹

Statistical analysis

Assessment of pooled mean and hazard ratio (HR) with 95% confidence interval (CI) was done using binary random-effect model, as appropriate. For continuous variables, Wan et al.’s³² and Hozo et al.’s³³ statistical methods were used to convert median (IQR) and median (range) to mean ± SD, respectively. We utilized forest plots to illustrate mean PLR levels based on studies reported follow-up, in-hospital, and overall mortality (including follow-up and in-hospital death). We further analyzed PLR mean according to HF status as well as death or survival status. Also, forest plot was depicted to assess the association between PLR and mortality HR. Cochran’s Q statistic, I², and tau squared (τ²) were measured to evaluate heterogeneity. We evaluated publication bias through funnel plots, Egger’s and Begg’s tests, as well as Duval and Tweedie’s trim-and-fill method. To assess the robustness of the findings, sensitivity analysis using leave-one-out method was done. We used an Excel datasheet to enter all recruited data, and comprehensive meta-analysis software (version 2.0, Biostat, Englewood, NJ) and RStudio software (version 1.1.463, RStudio, Inc., Boston, MA) were statistical tools used to perform the analyses.

Results

We found 482 records during primary literature review. After elimination of 158 duplicated records, 324 records were investigated. Based on inclusion/exclusion criteria, a total of 21 studies on 13,924 patients were selected for final analysis (Figure 1). The total population had a mean age of 70.36 ± 12.88 years (n = 20 studies) and 61.72% were males. Summary of included studies is shown in Table 1. Except for three records, all other articles had cross-sectional designs.^34–36 Risk of bias and quality assessments of recruited articles are provided in Supplemental Tables S1 and S2. In total, 20 articles (n = 10,674) reported PLR levels. The mean PLR in total HF population was found to be 165.54 (95% CI: 154.69–176.38) (Figure 2). We also provided results of heterogeneity indices in Table 2. In terms of publication bias, funnel plot is presented in Supplemental Figure S1. Although Begg’s test (p = 0.054) was in favor of no publication bias, the result of Egger’s test was the opposite (p = 0.0004). Duval and Tweedie’s trim-and-fill method revealed potential five missing studies [observed point estimate: 165.54 (95% CI: 154.69–176.38), adjusted point estimate: 151.59 (95% CI: 141.28–161.89)]. To assess the robustness of the findings, a sensitivity analysis was performed, indicating the consistency of the outputs (Supplemental Figure S2).

Table 1.

Summary of included studies reporting PLR and HF clinical outcomes.

Authors	Design	Sample size		Male (%)	Age	Follow-up duration	PLR	Outcomes
Wu et al.³⁵	Cohort	Total	1207	689 (57.08)	Mean ± SD: 67.30 ± 12.50	Median (IQR): 66 (35–105.5) months	Mean ± SD: 121.03 ± 50.02 Median (IQR): 116.70 (89.50–156.90) PLR quartiles: Q1: PLR < 89.5 Q2: 89.5 ⩽ PLR < 116.7 Q3: 116.7 ⩽ PLR < 156.9 Q4: PLR ⩾ 156.9	Follow-up mortality: 540 (44.73%)
		Survival	667	363 (54.42)	Mean ± SD: 63.30 ± 13.00		Mean ± SD: 114.16 ± 43.23 Median (IQR): 111.30 (86.50–144.70)
		Death	540	326 (60.37)	Mean ± SD: 72.20 ± 9.90		Mean ± SD: 130.23 ± 55.97 Median (IQR): 124.60 (95.40–170.70)
Tamaki et al.³⁶	Cohort	Total	1026	462 (45.03)	Mean ± SD: 82.33 ± 7.42 Median (IQR): 83 (77–87)	Median: 429 days	Mean ± SD: 174.66 ± 100.96 Median (IQR): 166 (111–247) PLR cutoff: 193	Follow-up mortality: - All-cause death: 195 (19%) - CVD death: 85 (8.28%)
Çakır et al.³⁷	Cross-sectional	Total	180	78 (43.33)	Mean ± SD: 73.15 ± 9.33 Median (IQR): 75 (67–80)	Mean: 1 year	Mean ± SD: 119.98 ± 52.74	Follow-up mortality: 33 (18.33%)
		Survival	147	63 (42.86)		Mean ± SD: 73.10 ± 9.50	Mean ± SD: 113.96 ± 49.18 Median (IQR): 112.8 (81.7–147.4)
		Death	33	15 (45.45)		Mean ± SD: 73.40 ± 8.70	Mean ± SD: 146.80 ± 60.13 Median (IQR): 140.8 (111–188.6)
Liu et al.³⁸	Cross-sectional	Total	550	262 (47.64)	NR	Median: 20.25 months	Mean ± SD: 142.15 ± 63.80 Median (IQR): 135.54 (102.53–188.38)	Follow-up death and readmission: 330 (60%)
		No adverse event	220	NR	NR		Mean ± SD: 125.60 ± 51.83 Median (IQR): 123.16 (92.09–161.55)
		Positive adverse event	330	NR	NR		Mean ± SD: 150.88 ± 65.62 Median (IQR): 147.85 (108.33–196.47)
Kocaoglu et al.³⁹	Cross-sectional	Total	101	49 (48.51)	Mean ± SD: 73.15 ± 10.19	Mean: 3 months	Mean ± SD: 232.99 ± 169.93	Follow-up mortality: 39 (38.60%)
		Survival	62	30 (48.39)	Mean ± SD: 72.61 ± 10.92		Mean ± SD: 229.33 ± 165.36 Median (IQR): 198.80 (135.66–353.54)
		Death	39	19 (48.72)	Mean ± SD: 74.00 ± 8.96		Mean ± SD: 238.83 ± 179.01 Median (IQR): 215 (139–362.50)
Davran et al.⁴⁰	Cross-sectional	Total	139	64 (46.04)	Mean ± SD: 69.20 ± 12.10	Mean: 1 year	Mean ± SD: 184.69 ± 93.85	Follow-up mortality: 14 (10.07%) In-hospital mortality: 9 (6.47%)
		Survival	116	57 (49.13)	Mean ± SD: 69.30 ± 11.50		Mean ± SD: 177.4 ± 93.4
		Death	23	7 (30.43)	Mean ± SD: 69.20 ± 15.00		Mean ± SD: 221.5 ± 89.2
Heidarpour et al.²⁶	Cross-sectional	Total	405	274 (67.65)	Mean ± SD: 65.9 ± 13.49	Mean ± SD: 4.26 ± 2.2 months	Mean ± SD: 128.89 ± 105.58 PLR quartiles: Q1: PLR ⩽ 118 Q2: 119 < PLR ⩽ 198 Q3: 198 < PLR < 268 Q4: PLR ⩾ 268	Follow-up mortality: 44 (10.86%) Rehospitalization: 98 (24.15%)
		Survival	361	246 (68.14)	Mean ± SD: 65.79 ± 13.22		Mean ± SD: 129.03 ± 108.6
		Death	44	28 (63.64)	Mean ± SD: 67.57 ± 15.58		Mean ± SD: 127.8 ± 77.5
Delcea et al.²⁷	Cross-sectional	Total	1299	624 (48.04)	Mean ± SD: 72.35 ± 10.45	NA	Mean ± SD: 135.49 ± 60.49 Median (IQR): 129.61 (97.68–179.19) PLR tertiles: T1: 14.26–108.02 T2: 108.07–154.78 T3: 154.80–992.88 PLR cutoff: 154.78	In-hospital mortality: 37 (2.84%)
Dahlen et al.²⁸	Cross-sectional	Total	3250	2066 (63.57)	Mean ± SD: 64.57 ± 11.09	Median (IQR): 2.24 (1.18–3.97) years	NR	Worsening of HF: 298 out of 3220 (9.25%)
Arfsten et al.⁴¹	Cross-sectional	Total	443	325 (73.36)	Mean ± SD: 63.33 ± 14.13 Median (IQR): 65 (53–72)	Median (IQR): 21 (10–28) months	Mean ± SD: 152.33 ± 68.42 Median (IQR): 145 (110–202)	Follow-up mortality: 75 (17%)
Sadeghi et al.⁴²	Cross-sectional	Total	197	121 (61.42)	Mean ± SD: 66.31 ± 14.97	Mean: 6 months	Mean ± SD: 146.19 ± 120.498 PLR cutoff: 150	Follow-up mortality: 30 (15.22%)
		Survival	167	110 (65.78)	Mean ± SD: 65.9 ± 14.64		Mean ± SD: 132.81 ± 81.18
		Death	30	11 (36.67)	Mean ± SD: 68.63 ± 16.82		Mean ± SD: 220.72 ± 233.3
Ye et al.³⁴	Cohort	Total	443	149 (33.63)	Mean ± SD: 72.07 ± 6.81	Mean (range): 143.68 (20–180) days	Mean ± SD: 149.66 ± 52.37 PLR quartiles: Q1: PLR < 110.63 Q2: 110.63 ⩽ PLR < 139.23 Q3: 139.23 ⩽ PLR ⩽ 177.17 Q4: PLR > 177.17	Follow-up mortality: 160 (36.12%)
		Survival	283	100 (35.34)	Mean ± SD: 72.34 ± 6.8		Mean ± SD: 136.02 ± 45.89
		Death	160	49 (30.63)	Mean ± SD: 71.61 ± 6.84		Mean ± SD: 173.81 ± 54.51
Turcato et al.⁴³	Cross-sectional	Total	439	247 (56.26)	Mean ± SD: 81.51 ± 8.25	Mean: 30 days	Mean ± SD: 182.13 ± 107.02 Median (IQR): 171.1 (115.7–259.6) PLR cutoff: 272.9	Follow-up mortality: 45 (10.93%)
		Survival	394	221 (56.09)	Median (IQR): 82 (75–86)		Mean ± SD: 170.8 ± 97.84 Median (IQR): 159.1 (110.9–242.4)
		Death	45	26 (57.78)	Median (IQR): 86 (81–91)		Mean ± SD: 313.73 ± 212.2 Median (IQR): 285.9 (189.1–466.2)
Siedlecki et al.⁴⁴	Cross-sectional	Total	367	278 (75.75)	Mean ± SD: 63.3 ± 10.8	Mean ± SD: 4.4 ± 1.3	Mean ± SD: 132.76 ± 74.97	Follow-up mortality: 197 (53.67%)
		Survival	170	121 (71.18)	Median (IQR): 62 (56–70.4)		Mean ± SD: 80.65 ± 25.82 Median (IQR): 81.62 (62.9–97.44)
		Death	197	157 (79.70)	Median (IQR): 63.2 (56.6–73.3)		Mean ± SD: 177.74 ± 74.38 Median (IQR): 168.22 (132.7–232.31)
Yurtdaş et al.⁴⁵	Cross-sectional	Total	40	19 (47.50)	Mean ± SD: 67.71 ± 10.2	NA	Mean ± SD: 179 ± 125 PLR cutoff: 73	HF prediction: OR: 1.015 (95% CI: 1.001–1.028)
Demir et al.⁴⁶	Cross-sectional	Total	115	65 (56.52)	Mean ± SD: 69.3 ± 11.8	Mean ± SD: 20.8 ± 16.1	Mean ± SD: 191.3 ± 164 PLR tertiles: T1: <98.3 T2: 98.3–194.97 T3: >194.97	In-hospital mortality: 10 (8.69%) Follow-up mortality: 39 (33.91%)
		Survival	66	38 (57.58)	Mean ± SD: 66.2 ± 12.2		Mean ± SD: 248.32 ± 191.92 Median (range): 112.5 (20.3–748)
		Death	49	27 (55.10)	Mean ± SD: 73.5 ± 10		Mean ± SD: 308.45 ± 209.75 Median (range): 186.4 (11–850)
Szyguła-Jurkiewicz et al.⁴⁷	Cross-sectional	Total	785	603 (76.82)	Mean ± SD: 61.9 ± 12.4	Mean: 3 years	Mean ± SD: 123.1 ± 59.1	Follow-up mortality: 363 (46.24%)
		Survival	422	312 (73.93)	Median (IQR): 60.9 (52.8–69.4)		Mean ± SD: 95.3 ± 35.7 Median (IQR): 92.7 (72.6–120.6)
		Death	363	291 (80.17)	Median (IQR): 63.1 (55.6–74.4)		Mean ± SD: 155.43 ± 64.3 Median (IQR): 150.9 (114.5–200.9)
Pourafkari et al.⁴⁸	Cross-sectional	Total (in-hospital)	554	531 (95.85)	Mean ± SD: 76.47 ± 11.62	NA	Mean ± SD: 208.9 ± 134.59	In-hospital mortality: 31 (5.59%)
		Survival	523	500 (95.60)	Mean ± SD: 76 ± 11.6		Mean ± SD: 207 ± 129
		Death	31	31 (100.00)	Mean ± SD: 84.5 ± 8.8		Mean ± SD: 241 ± 208
		Total (long-term)	333	319 (95.80)	Mean ± SD: 76.64 ± 11.46	NR	Mean ± SD: 216.67 ± 177.27 PLR tertiles: T1: <137 T2: 137–210 T3: >210	Follow-up mortality: 198 (59.45%)
		Survival	135	127 (94.07)	Mean ± SD: 72.9 ± 11		Mean ± SD: 181 ± 110
		Death	198	192 (96.97)	Mean ± SD: 79.2 ± 11.1		Mean ± SD: 241 ± 208
Huang et al.⁴⁹	Cross-sectional	Total	1923	1307 (67.97)	Mean ± SD: 76 ± 12	Mean ± SD: 28.6 ± 20.7 months	Mean ± SD: 188.59 ± 189.53	Follow-up mortality: 875 (45.50%)
		Survival	1048	697 (66.51)	Mean ± SD: 74.8 ± 13.9		Mean ± SD: 173.1 ± 120.9
		Death	875	610 (69.71)	Mean ± SD: 78.3 ± 10.7		Mean ± SD: 207 ± 246.7
Siniorakis et al.⁵⁰	Cross-sectional	Total	72	31 (43.05)	Mean ± SD: 77 ± 10	NA	Mean ± SD: 252 ± 164	In-hospital mortality: 3 (4.2%)
Durmus et al.⁵¹	Cross-sectional	Total	56	32 (57.14)	Mean ± SD: 67.5 ± 12.6	Mean ± SD: 12.8 ± 7.6 months	Mean ± SD: 197 ± 103 PLR cutoff: 137.3	HF prediction: OR: 0.993 (95% CI: 0.976–1.010)

Open in a new tab

CI, confidence interval; CVD, cardiovascular disease; HF, heart failure; IQR, interquartile range; NA, not applicable; NR, not reported; OR, odds ratio; PLR, platelet-to-lymphocyte ratio; Q, quartile; SD, standard deviation; T, tertile.

Figure 2. — Forest plot for mean PLR based on total population.

PLR, platelet-to-lymphocyte ratio.

Table 2.

Heterogeneity results of included studies according to PLR.

Outcome of interest	Q ^a	I^{2 b} (%)	τ ^{2 c}	p
Mean PLR	959.256	97.91	571.310	<0.001
Total mortality	954.145	98.11	625.472	<0.001
Follow-up mortality	765.676	98.04	666.969	<0.001
In-hospital mortality	221.423	98.19	2248.197	<0.001
PLR difference (survival versus death)	280.537	95.36	0.234	<0.001
Mortality hazard ratio	16.810	82.15	0	0.001
Mean PLR in HFrEF	118.242	94.92	330.965	<0.001
PLR difference in HFrEF (survival versus death)	39.353	89.83	0.185	<0.001

Open in a new tab

Cochran’s Q statistic for heterogeneity.

Index for the degree of heterogeneity.

Tau-squared measure of heterogeneity.

HFrEF, heart failure with reduced ejection fraction; PLR, platelet-to-lymphocyte ratio.

PLR and mortality

In total, 18 articles (n = 10,084) were found to report PLR levels and mortality (including either follow-up or in-hospital death) in HF patients.^{26,27,34–37,39–44,46–51} The mean age was 72.24 ± 12.87 years (males: 61.95%). Figure 3 depicts a forest plot of PLR means in enrolled records reported mortality. The total PLR mean was 166.68 (95% CI: 154.87–178.50). The heterogeneity results are provided in Table 2. The funnel plot is also provided in Supplemental Figure S3. Begg’s (p = 0.046) and Egger’s (p = 0.0006) tests were in favor of potential publication bias. Moreover, the results of Duval and Tweedie’s trim-and-fill method showed the presence of two missing studies [observed point estimate: 166.68 (95% CI: 154.87–178.50), adjusted point estimate: 160.46 (95% CI: 148.98–171.94)]. However, sensitivity analysis confirmed the robustness of our findings (Supplemental Figure S4).

Figure 3. — Forest plot for mean PLR based on studies reported mortality (follow-up or in-hospital mortality).

PLR, platelet-to-lymphocyte ratio.

In total, 16 records (n = 8159) reported follow-up mortality in HF sufferers.^{26,34–37,39–44,46–49,51} Follow-up duration ranged from 30 days to 66 (IQR: 35–105.5) months.^35,43 This subset of the population had a mean age of 71.90 ± 13.26 years (males: 62.04%). The mean PLR was found to be 162.55 (95% CI: 149.35–175.75) (Figure 4). Heterogeneity indices were quite considerable (Table 2). For publication bias, the funnel plot is shown in Supplemental Figure S5. Despite Begg’s test did not show any bias (p = 0.196), Egger’s test (p = 0.002) was in favor of potential publication bias. However, the results of Duval and Tweedie’s trim-and-fill method showed similar observed and adjusted point estimates, indicating no missing studies. We also performed the sensitivity analysis to find the impact of any included study on the findings. As shown in Supplemental Figure S6, our findings were robust.

Figure 4. — Forest plot for mean PLR based on studies reported follow-up mortality.

PLR, platelet-to-lymphocyte ratio.

Five articles on 2179 subjects reported in-hospital mortality rate (mean age: 73.18 ± 11.16 years, males: 60.34%) with PLR levels of 192.83 (95% CI: 150.06–235.61) (Figure 5).^{27,40,46,48,50} We provided heterogeneity outputs and the funnel plot in Table 2 and Supplemental Figure S7, respectively. Begg’s and Egger’s p values were 0.403 and 0.035, respectively. In addition, Duval and Tweedie’s trim-and-fill method did not show any missing studies (similar observed and adjusted point estimates). Sensitivity analysis indicated the consistency of the findings (Supplemental Figure S8).

Figure 5. — Forest plot for mean PLR based on studies reported in-hospital mortality.

PLR, platelet-to-lymphocyte ratio.

PLR based on survival and death subgroups

We found 13 records (n = 7188) reported PLR means in survived or dead HF patients.^{26,34,35,37,39,40,42–44,46–49} PLR mean differed significantly between groups in a way that deceased subjects had higher PLR levels in comparison to survived group [194.73 (95% CI: 175.60–213.85) versus 152.34 (95% CI: 134.01–170.68)] (Figure 6). The standard PLR mean difference was also remarkable. Survived patients had significantly lower PLR ranges compared to deceased ones [−0.592 (95% CI: −0.857, −0.326), p < 0.001] (Figure 7). A funnel plot to demonstrate publication bias is depicted in Supplemental Figure S9 with a further report of heterogeneity indices in Table 2. Neither Begg’s (p = 0.292) nor Egger’s (p = 0.169) test was in favor of any publication bias. However, Duval and Tweedie’s trim-and-fill method revealed different observed [−0.592 (95% CI: −0.857, −0.326)] and adjusted [−0.682 (95% CI: −0.941, −0.423)] point estimates, indicating the presence of two missing studies. Complementary sensitivity analysis confirmed the robustness of the outcomes (Supplemental Figure S10).

Figure 6. — Forest plot for mean PLR based on studies reported death and survival groups.

PLR, platelet-to-lymphocyte ratio.

Figure 7. — Forest plot for standard PLR mean difference in survived *versus* deceased patients.

PLR, platelet-to-lymphocyte ratio.

PLR and mortality HR

Six articles reported PLR impact (as a continuous variable) on HR of follow-up death.^{35,38,41,42,44,49} However, two records were not included in the analysis due to inconsistent CIs.^35,44 Figure 8 shows a forest plot of PLR effect on mortality. Our findings failed to reveal any significant association between increased PLR and long-term mortality risk (HR: 1.02, 95% CI: 0.99–1.05, p = 0.289) (heterogeneity indices: Table 2).

Figure 8. — Forest plot for PLR (as a continuous variable) mortality hazard ratio.

PLR, platelet-to-lymphocyte ratio.

Two studies reported multi-variable adjusted mortality HR based on PLR tertiles.^46,48 Data analysis revealed risk of death was not statistically significant across higher tertiles in comparison to the first PLR tertile (T2 versus T1: HR: 1.74, 95% CI: 0.81–3.72, p = 0.156 and T3 versus T1: HR: 2.40, 95% CI: 0.46–12.60, p = 0.300) (Figure 9). Three other studies reported mortality risk according to PLR quartiles.^26,34,35 With the exclusion of one study due to inconsistent CIs,³⁴ complementary analysis did not find any significant association between PLR quartiles (Q2 versus Q1: HR: 0.72, 95% CI: 0.29–1.81, p = 0.483, Q3 versus Q1: HR: 1.01, 95% CI: 0.56–1.83, p = 0.982, and Q4 versus Q1: HR: 1.09, 95% CI: 0.83–1.43, p = 0.523) (Figure 10).

Figure 9. — Forest plot of mortality hazard ratio according to PLR tertiles.

PLR, platelet-to-lymphocyte ratio.

Figure 10. — Forest plot of mortality hazard ratio according to PLR quartiles.

PLR, platelet-to-lymphocyte ratio.

PLR and rehospitalization

One record reported a readmission rate. Heidarpour et al. implemented a cross-sectional study to investigate the role of PLR in rehospitalization among HF patients. After a follow-up duration of 4.26 ± 2.2 months, 98 out of 405 (24.2%) acute decompensated HF (ADHF) patients were readmitted. The rehospitalization distribution across PLR quartiles were the following: Q1: 24 out of 100 (24%), Q2: 28 out of 103 (27.2%), Q3: 30 out of 101 (29.7%), and Q4: 16 out of 101 (15.8%). However, the difference was not statistically significant (p = 0.111).²⁶

PLR and HF detection

Two articles evaluated the PLR capability to predict HF. Yurtdaş and colleagues enrolled 40 HF individuals with left ventricular ejection fraction (LVEF) of less than 40% and compared the PLR levels with 30 healthy controls (LVEF ⩾ 50%). Mean PLR differed significantly between patients and controls (179 ± 125 versus 101 ± 82, p = 0.004). Results of multivariate regression analysis suggested PLR could be an HF predictor [odds ratio (OR): 1.015, 95% CI: 1.001–1.028, p = 0.030].⁴⁵ On the other hand, Durmus et al. enrolled 56 decompensated HF regardless of LVEF and 40 controls to assess whether PLR was capable of independently predicting HF. However, multi-variable logistic regression analysis failed to prove any capability in this regard (OR: 0.993, 95% CI: 0.976–1.010, p = 0.407).⁵¹

PLR and HF worsening

One record reported the potential impact of PLR on HF worsening. After a median follow-up of 2.24 (IQR: 1.18–3.97) years, 298 out of 3220 patients experienced worsening of HF, and the multivariate Cox regression model indicated PLR >75th percentile had been associated with 1.50 (95% CI: 1.17–1.93, p = 0.0013) times increased risk of cardiac function deterioration.²⁸

PLR quartiles and HF outcomes

Four records reported specific PLR quartiles.^16,28,34,35 In Wu et al.’s study, the following were defined as PLR quartiles for 1207 HF subjects: Q1: PLR < 89.5, Q2: 89.5 ⩽ PLR < 116.7, Q3: 116.7 ⩽ PLR < 156.9, and Q4: PLR ⩾ 156.9. After the median follow-up for 66 months, they found significantly higher HR of all-cause death among those within the highest quartile compared to the first PLR quartile in the univariate model but not in the multivariate one (1.45, 95% CI: 1.14–1.83, p = 0.002, and 1.09, 95% CI: 0.83–1.44, p = 0.5, respectively). The area under the curve (AUC) for the prediction of all-cause mortality was 0.58 (95% CI: 0.55–0.61) with a sensitivity and specificity of 18.7% and 89.8%, respectively.³⁵ In a total of 405 ADHF individuals, the following were defined as PLR quartiles: Q1: PLR ⩽ 118 (n = 100), Q2: 119 < PLR < 198 (n = 103), Q3: 198 ⩽ PLR < 268 (n = 101), and Q4: PLR ⩾ 268 (n = 101). They assessed mortality and rehospitalization occurrence during a mean follow-up duration of 4.26 (SD: 2.2) months and 44 (10.9%) deaths and 98 (24.2%) readmissions were observed, respectively [deaths: Q1: 14 (14%), Q2: 8 (7.8%), Q3: 11 (10.9%), and Q4: 11 (10.9%), rehospitalizations: Q1: 24 (24%), Q2: 28 (27.2%), Q3: 30 (29.7%), and Q4: 16 (15.8%)]. However, neither death (p = 0.565) nor readmission (p = 0.111) differed remarkably across PLR quartiles. Multivariate adjusted Cox regression analysis failed to prove any differences between the first PLR quartile compared to the others (Q2 versus Q1: HR: 0.40, 95% CI: 0.16–1.01, p = 0.054, Q3 versus Q1: HR: 0.61, 95% CI: 0.23–1.56, p = 0.305, and Q4 versus Q1: HR: 1.12, 95% CI: 0.34–3.72, p = 0.845).²⁶ In another study, the followings were considered for PLR quartiles in 443 acute HF individuals [Q1: <110.63 (n = 111), Q2: 110.63–139.23 (n = 112), Q3: 139.23–177.17 (n = 110), and Q4: >177.17 (n = 110)]. The mean follow-up time was 143.68 days (range: 20–180 days) and 160 (36.12%) deaths occurred [Q1: 11 (9.90%), Q2: 26 (23.21%), Q3: 54 (49.09%), and Q4: 69 (62.72%), p < 0.001]. Six-month survival rates according to consecutive PLR quartiles were 90.09%, 76.79%, 50.07%, and 32.27%. Multivariable Cox regression model revealed patients within the third and fourth PLR quartiles had 3.118 (95% CI: 1.668–5.386, p < 0.001) and 2.437 (95% CI: 1.302–3.653, p < 0.001) times higher risk of mortality in comparison to the first quartile, respectively.³⁴ Another study on 3250 HF subjects indicated that the fourth PLR quartile (>75th percentile) had been associated with 1.50 (95% CI: 1.17–1.93) times increased risk of HF worsening.²⁸

PLR tertiles and HF outcomes

Four records were found to assess clinical outcomes in HF according to PLR tertiles.^27,43,46,48 Delcea and colleagues enrolled 1299 HF patients and categorized them based on PLR tertiles [T1: 14.26–108.02 (n = 433), T2: 108.07–154.78 (n = 433), and T3: 154.80–992.88 (n = 433)]. In total, 37 (2.84%) deaths occurred during hospitalization with the following distribution: T1: 6 (1.4%), T2: 10 (2.3%), and T3: 21 (4.8%), p = 0.006. They also assessed the length of hospital stay and found significant differences between PLR tertiles [T1: median (IQR): 5 (3–7), T2: median (IQR): 5 (4–7), and T3: median (IQR): 6 (4–9)].²⁷ Likewise, 115 HF patients who suffered from acute cardiogenic pulmonary edema were selected by Demir and colleagues. PLR tertiles were defined as low [PLR < 98.3 (n = 38)], medium [98.3–194.97 (n = 39)], and high [PLR > 194.97 (n = 38)]. In total, 10 (8.7%) patients died during their admissions [T1: 2 (8%), T2: 3 (11.5%), and T3: 5 (20%)] but the difference was not significant compared to survivors (p = 0.435). They followed participants for 20.8 ± 16.1 months and 39 (37.14%) deaths were observed [T1: 7 (17.94%), T2: 14 (35.89%), and T3: 18 (46.15%)]. Further assessment of total mortality revealed a significant PLR difference between deceased and survived subjects (p = 0.005). Multivariate Cox regression model indicated that patients within second and third PLR tertiles had higher mortality risk compared to the first tertile (T2 versus T1: HR: 2.730, 95% CI: 1.198–6.221, p = 0.017, T3 versus T1: HR: 5.657, 95% CI: 2.467–12.969, p < 0.001).⁴⁶ Pourafkari and colleagues determined PLR tertiles as T1: PLR < 137, T2: 137–210, and T3: PLR > 210. During the follow-up, 198 out of 354 HF patients died and Cox regression multivariate analysis for follow-up death did not show any considerable risk differences between the second and third tertiles compared to the first one (T2 versus T1: HR: 1.243, 95% CI: 0.707–2.184 and T3 versus T1: HR: 1.043, 95% CI: 0.497–2.188).⁴⁸ Thirty-day mortality rates for 45 out of 439 ADHF patients based on PLR tertiles were the following: T1: 4 (8.9%), T2: 13 (28.9%), and T3: 28 (62.2%). The difference was statistically significant compared to PLR tertiles among survivors (p < 0.001). They found that PLR tertiles could predict 30-day mortality in ADHF patients (multivariate adjusted OR: 1.94, 95% CI: 1.12–3.35, p = 0.018).⁴³

PLR cutoff and HF outcomes

Six records reported unique PLR cutoff values.^{27,36,42,43,45,51} Tamaki and colleagues reported a PLR cutoff point of 193 for cardiovascular death prediction in 1026 HF with preserved ejection fraction (HFpEF) patients (AUC: 0.60, 95% CI: 0.57–0.63, p = 0.0009, sensitivity: 56%, specificity: 62%). They found that patients who had higher PLR values experienced cardiovascular and all-cause death more frequently (HR: 1.76, 95% CI: 1.09–2.84, p = 0.0215, and HR: 1.47, 95% CI: 1.06–2.03, p = 0.0198, respectively).³⁶ Delcea et al.²⁷ found PLR cutoff point of 154.78 could reliably predict in-hospital mortality (sensitivity: 54.05%, specificity: 67.40%, AUC: 0.658, 95% CI: 0.567–0.750, p = 0.001) and extended length of hospital stay, defined as hospitalization for at least 7 days (sensitivity: 49.13%, specificity: 71.43%, AUC: 0.626, 95% CI: 0.587–0.666, p < 0.001). The cutoff was reported to be 150 in Sadeghi et al.’s study (sensitivity: 51.7%, specificity: 72.3%, positive predictive value: 25.9%, negative predictive value: 88.9%, accuracy: 69%, AUC: 0.62, p = 0.041). In multivariate Cox regression analysis, PLR did not show any predictability for 30-day survival (HR: 1.006, 95% CI: 0.99–1.021, p = 0.487).⁴² In another study, 272.9 was set to be the optimal PLR cutoff (AUC: 0.71) and patients within the higher PLR group (n = 94) had 3.22 (95% CI: 1.56–5.68, p < 0.001) times increased odds of 30-day mortality compared to the lower group (n = 345). Also, increased PLR was mostly observed in deceased subjects compared to the survivors (55.6% versus 17.5%, p < 0.001).⁴³ Yurtdaş et al.⁴⁵ found the PLR cutoff point of 73 (sensitivity: 83%, specificity: 53%, AUC: 0.76, 95% CI: 0.65–0.88, p < 0.001) as an optimal value to predict HF. Another HF predicting cutoff point was found to be 137.3 in Durmus et al.’s study (sensitivity: 70%, specificity: 60%, AUC: 0.689, p = 0.004).⁵¹

PLR and HF status

Seven studies (n = 2151) investigated the PLR impact on HF patients with reduced ejection fraction (HFrEF).^{37,40–42,44,45,47} In terms of HFpEF, there was only one study³⁶ and others included all HF patients regardless of LVEF.^{26–28,34,35,37–39,43,46,48,49,51} Figure 11 presents the forest plot for PLR mean in HFrEF individuals. The mean was 144.96 (95% CI: 130.38–159.54). Heterogeneity indices are provided in Table 2. Neither Begg’s nor Egger’s test results indicated publication bias [p = 0.274, and p = 0.077, respectively, funnel plot (Supplemental Figure S11)]. However, Duval and Tweedie’s trim-and-fill method suggested that two studies were missing (observed point estimate: 144.96, 95% CI: 130.38–159.54, adjusted point estimate: 134.48, 95% CI: 119.88–149.08). To assess the impact of each study on the findings, we performed a sensitivity analysis and the results supported the robustness of our results (Supplemental Figure S12).

Figure 11. — Forest plot for mean PLR in patients with heart failure with reduced ejection fraction.

PLR, platelet-to-lymphocyte ratio.

Five studies reported PLR values among survived and deceased subjects who suffered from HFrEF. Complementary analyses revealed live patients had significantly lower PLR mean compared to the death group (standard mean difference: −0.979, 95% CI: −1.384 to −0.573, p < 0.001) (Figure 12). Table 2 shows the heterogeneity indices of the included studies. The funnel plot, as shown in Supplemental Figure S13, did not reveal any publication bias, also confirmed by Begg’s (p = 0.110) and Egger’s (p = 0.152) tests. Duval and Tweedie’s trim-and-fill method was in favor of one missing study (observed point estimate: −0.979, 95% CI: −1.384 to −0.573, adjusted point estimate: −1.090, 95% CI: −1.461 to −0.718). Sensitivity analysis indicated our findings were robust and consistent (Supplemental Figure S14).

Figure 12. — Forest plot for standard PLR mean difference in survived *versus* deceased heart failure with reduced ejection fraction patients.

PLR, platelet-to-lymphocyte ratio.

Discussion

The main aim of this systematic review and meta-analysis was to evaluate the impact of PLR on clinical outcomes among patients suffering from HF. The PLR mean in the total HF population was determined to be 165.54 (95% CI: 154.69–176.38). We also found patients who died in the context of HF had significantly higher PLR levels rather than survivors [194.73 (95% CI: 175.60–213.85) versus 152.34 (95% CI: 134.01–170.68)]. This trend was also observed for patients with HfrEF. However, further analysis was not significant in terms of PLR association with mortality risk, either as a continuous or as a categorical (tertiles or quartiles) variable. Since HF prevalence increases as people age and it is associated with considerable mortality rate and poor prognosis despite HF management progress, this index should be used cautiously to evaluate clinical outcomes and subsequently prioritize high-risk individuals.

Inflammation has been reported to play pivotal roles in the pathogenesis of CVDs through different mechanisms including oxidative stress and cell-mediated immune responses.^52,53 Although the exact pathophysiological mechanism for HF is still not defined, several theoretical mechanisms are proposed. The first one is related to inflammatory cytokines. Inflammation leads to the production and secretion of various cytokines like interleukin (IL)-4, IL-6, C-reactive protein, and tumor necrosis factor-α.^54–56 These cytokines result in negative effects on cardiac cells ultimately leading to decreased cardiac pump function.^54,55 Also, different cells are involved in HF pathogenesis. Some culprit cells are platelets, monocytes, and neutrophils. They initiate inflammatory cytokine production and secretion, involve other cell lines, and alter receptor expression to induce additional inflammatory markers to continue disease progression.^57,58 Platelets can interact with both leukocytes and endothelial cells. They perform their functions by production of inflammatory factors, subsequently leading to monocyte transmigration. They are called to be a link between thrombosis, inflammation, and atherosclerosis pathogenesis.^59,60 In addition to increased platelet counts due to inflammatory cytokines, thrombocytopenia was also observed in HF patients. Getawa and Bayleyegn’s⁶¹ study on 245 HF sufferers showed thrombocytopenia was a more prevalent platelet abnormality in comparison to thrombocytosis [12.24% (95% CI: 8.67–17.01%) versus 2.86% (95% CI: 1.36–5.90%)]. Lower platelets have been reported to be an independent prognostic tool. Mojadidi et al.⁶² found HF individuals with moderate to severe thrombocytopenia (platelets <100,000/µl) had 1.84 (95% CI: 1.33–2.56, p < 0.001) times higher 1-year all-cause mortality risk in comparison to those with normal or mild thrombocytopenia. Although the exact etiology of this phenomenon remains to be elucidated, one possible explanation might be related to abnormal immune response due to medication therapy.⁶³ It seems both increased or decreased platelet counts might be effective in HF prognosis. However, data are still limited, and further studies are warranted.

On the other hand, lymphocytes have been reported to be a protective factor in inflammation through tissue inhibitors of metalloproteinase-1 expression.⁶⁴ However, lymphocyte counts could be reduced during physiologic stress. High cortisol and catecholamine secretion through the hypothalamus–hypophysis–adrenal axis during a stressful condition, like HF, could reduce lymphocytes by redistribution of them to lymphatic organs, finally leading to a decrease in survival rate.^26,65 Moreover, HF sufferers have lower flow in coronary vessels and higher platelets could lead to a pro-thrombotic state and worsen the prognosis.^37,66 Therefore, high platelet counts and low lymphocytes result in raised PLR and this inexpensive blood index might be practical in clinical settings to assess prognosis.

Another considerable factor is HF subtypes. We found a PLR mean of 144.96 (95% CI: 130.38–159.54) in HFrEF and deceased subjects had higher values in comparison to the survived subjects. Despite the probable role of inflammation in HF has been widely investigated in recent years, the exact mechanism might differ according to HF types. For instance, one of the main triggers in HFrEF is myocardial injury, resulting in the initiation of inflammatory responses mediated by pro-inflammatory cytokines as well as the recruitment of immune cells. Subsequent collagen synthesis by myofibroblasts causes scar formation and reduction in LVEF.^67,68 This inflammatory response might be continued because of sustained myocardial injury or other mechanisms irrespective of tissue injury, including hemodynamic overload, activation of the renin–angiotensin–aldosterone, or sympathetic nervous system.⁶⁸

In terms of HFpEF, some underlying comorbidities, such as diabetes mellitus, hypertension, obesity, chronic kidney diseases, and chronic obstructive pulmonary disease initiate an inflammatory state leading to decreased nitric oxide bioavailability and increased production of reactive oxygen species. Further infiltration of blood monocytes into the cardiac tissue and their differentiation into macrophages cause impaired ventricular relaxation.^69,70

The role of biological pathways in different HF types is still being investigated. Tromp et al. evaluated 92 unique biomarkers in 1544 HF patients to find the correlations in HFrEF, HFpEF, and HF with midrange ejection fraction (HFmrEF). Network analysis revealed HFpEF biomarkers were mostly associated with inflammatory as well as extracellular matrix reorganization pathways. By contrast, common pathways in HFrEF were cardiac stretch, metabolism, and cellular proliferation. In terms of HFmrEF, the pathways were in between HFrEF and HFpEF.^71,72 Some specific inflammatory-related biomarkers suggested for HFpEF include Pentaxin-3 and receptor for the enhanced glycation end product.⁷² These result in interstitial fibrosis and cardiac cells stiffening, subsequently causing increased left ventricular filling pressure.^69,73

PLR has been suggested to negatively affect clinical outcomes in CVDs. A systematic review and meta-analysis indicated higher PLR had been associated with higher risk of both in-hospital and long-term major cardiovascular events in patients with acute coronary syndrome (ACS) [relative risk (RR): 1.95, 95% CI: 1.30–2.91, p = 0.001 and RR: 1.50, 95% CI: 1.08–2.09, p = 0.01, respectively].⁷⁴ This index has also been associated with the severity of atherosclerosis in ACS sufferers.⁷⁵

In the current study, we found higher PLR was mostly observed in deceased HF patients rather than survivors with a significant standard mean difference in alive versus dead patients (−0.592 (95% CI: −0.857 to −0.326), p < 0.001], and assessment of this index might be helpful for prognosis determination in clinical wards. However, complementary studies are still required. In terms of hospitalization, only one record reported no potential PLR effect on readmission among HF patients.²⁶ Since readmission poses a considerable economic burden, implementation of complementary studies is warranted to clarify this association. For HF detection, two records reported opposite outcomes.^45,51 In one study, PLR was found to be an acceptable HF predictor in comparison to healthy controls (OR: 1.015, 95% CI: 1.001–1.028).⁴⁵ On the other hand, PLR was not able to reliably predict HF in another record (OR: 0.992, 95% CI: 0.976–1.010, p = 0.407).⁵¹ Although the different HF types might be a potential explanation for this inconsistency, further studies are required to exactly evaluate the predictability of PLR for HF diagnosis. PLR and worsening of HF were evaluated in one record and the outcomes were in favor of increasing cardiac function deterioration with PLR > 75th percentile. However, no significant difference was found between two HF subtypes, including HFrEF and HFpEF.²⁸ Although one record is insufficient to generalize the outcomes, further studies might help to establish the PLR effect on HF worsening in different HF phenotypes.

PLR has been reported in different ways to assess HF clinical outcomes in multiple records including quartiles, tertiles, and optimum cutoff points. Different quartiles were the followings in recruited records: Q1: PLR < 89.5, Q2: 89.5 ⩽ PLR < 116.7, Q3: 116.7 ⩽ PLR < 156.9, and Q4: PLR ⩾ 156.9, Q1: PLR ⩽ 118, Q2: 119 < PLR < 198, Q3: 198 ⩽ PLR < 268, Q4: PLR ⩾ 268, and Q1: <110.63, Q2: 110.63–139.23, Q3: 139.23–177.17, Q4: >177.17.^26,34,35 PLR tertiles were also defined in three studies (T1: 14.26–108.02, T2: 108.07–154.78, T3: 154.80–992.88 and T1: PLR < 98.3, T2: 98.3–194.97, T3: PLR > 194.97 and T1: PLR < 137, T2: 137–210, T3: PLR > 210).^27,46,48 Moreover, different PLR cutoff points of 193, 154.78, 150, 272.9, 73, and 137.3 with different ranges of sensitivity, specificity, and AUC values have been reported to be used in HF detection and outcome assessment.^{27,36,42,43,45,51} This quite wide PLR cutoff range might be associated with different sample sizes and necessitates complementary studies to define the exact cutoff point.

This systematic review and meta-analysis is the first in the literature to evaluate the potential impact of PLR on HF outcomes. We tried our best to include every relevant record with no time limitation to increase the generalizability of our findings. However, some limitations are still present. We recruited only English-published articles and did not screen other non-English records and we were unable to compare PLR means based on gender. Enrolled studies had different sample sizes and study designs which disabled us from performing statistical analysis with low heterogeneity. Further analysis according to HF severity was not feasible due to the low number of records in each category. We were able to perform subgroup analysis on HFrEF patients and PLR comparison with other HF types was not feasible. Although no significant association was found in terms of PLR and mortality risk, the few number of recruited articles as well as the definition of different PLR tertiles or quartiles might explain this phenomenon.

In conclusion, despite PLR being a simple widely available tool and might be a good choice in nations with limited healthcare resources, this ratio should be investigated further for its predictability of HF clinical outcomes, and several other studies are warranted to explore this association, especially in terms of mortality risk.

Supplemental Material

sj-doc-2-tak-10.1177_17539447241227287 – Supplemental material for The impact of platelet-to-lymphocyte ratio on clinical outcomes in heart failure: a systematic review and meta-analysis

Click here for additional data file.^{(64KB, doc)}

Supplemental material, sj-doc-2-tak-10.1177_17539447241227287 for The impact of platelet-to-lymphocyte ratio on clinical outcomes in heart failure: a systematic review and meta-analysis by Mehrbod Vakhshoori, Niloofar Bondariyan, Sadeq Sabouhi, Keivan Kiani, Nazanin Alaei Faradonbeh, Sayed Ali Emami, Mehrnaz Shakarami, Farbod Khanizadeh, Shahin Sanaei, Niloofaralsadat Motamedi and Davood Shafie in Therapeutic Advances in Cardiovascular Disease

sj-docx-1-tak-10.1177_17539447241227287 – Supplemental material for The impact of platelet-to-lymphocyte ratio on clinical outcomes in heart failure: a systematic review and meta-analysis

Click here for additional data file.^{(374.7KB, docx)}

Supplemental material, sj-docx-1-tak-10.1177_17539447241227287 for The impact of platelet-to-lymphocyte ratio on clinical outcomes in heart failure: a systematic review and meta-analysis by Mehrbod Vakhshoori, Niloofar Bondariyan, Sadeq Sabouhi, Keivan Kiani, Nazanin Alaei Faradonbeh, Sayed Ali Emami, Mehrnaz Shakarami, Farbod Khanizadeh, Shahin Sanaei, Niloofaralsadat Motamedi and Davood Shafie in Therapeutic Advances in Cardiovascular Disease

Acknowledgments

None.

Footnotes

ORCID iD: Mehrbod Vakhshoori Inline graphic https://orcid.org/0000-0002-1380-4791

Supplemental material: Supplemental material for this article is available online.

Contributor Information

Mehrbod Vakhshoori, Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Hezar Jarib Avenue, Isfahan, Iran.

Niloofar Bondariyan, Department of Clinical Pharmacy, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.

Sadeq Sabouhi, Student Research Committee, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Keivan Kiani, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran.

Nazanin Alaei Faradonbeh, Department of Emergency Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.

Sayed Ali Emami, Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran.

Mehrnaz Shakarami, Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran.

Farbod Khanizadeh, Insurance Research Center, Tehran, Iran.

Shahin Sanaei, Department of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Niloofaralsadat Motamedi, Department of Pediatrics, Arak University of Medical Sciences, Arak, Iran.

Davood Shafie, Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran.

Declarations

Ethical approval and consent to participate: Not applicable.

Consent for publication: Not applicable.

Author contributions: Mehrbod Vakhshoori: Conceptualization; Formal analysis; Investigation; Methodology; Supervision; Writing – original draft; Writing – review & editing.

Niloofar Bondariyan: Conceptualization; Formal analysis; Methodology; Writing – original draft; Writing – review & editing.

Sadeq Sabouhi: Conceptualization; Data curation; Investigation; Writing – original draft; Writing – review & editing.

Keivan Kiani: Conceptualization; Methodology; Writing – original draft; Writing – review & editing.

Nazanin Alaei Faradonbeh: Conceptualization; Methodology; Writing – original draft; Writing – review & editing.

Sayed Ali Emami: Conceptualization; Data curation; Investigation; Methodology; Writing – original draft; Writing – review & editing.

Mehrnaz Shakarami: Conceptualization; Methodology; Writing – original draft; Writing – review & editing.

Farbod Khanizadeh: Conceptualization; Formal analysis; Methodology; Writing – original draft; Writing – review & editing.

Shahin Sanaei: Conceptualization; Methodology; Writing – original draft; Writing – review & editing.

Niloofaralsadat Motamedi: Conceptualization; Methodology; Writing – original draft; Writing – review & editing.

Davood Shafie: Conceptualization; Methodology; Supervision; Writing – original draft; Writing – review & editing.

Funding: The authors received no financial support for the research, authorship, and/or publication of this article.

The authors declare that there is no conflict of interest.

Availability of data and materials: The datasets generated during and/or analyzed during the current study are not publicly available due to confidential issues but are available from the corresponding author upon reasonable request.

References

1. Bozkurt B, Coats AJS, Tsutsui H, et al. Universal definition and classification of heart failure: a report of the Heart Failure Society of America, Heart Failure Association of the European Society of cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition of Heart Failure: Endorsed by the Canadian Heart Failure Society, Heart Failure Association of India, Cardiac Society of Australia and New Zealand, and Chinese Heart Failure Association. Eur J Heart Fail 2021; 23: 352–380. [DOI] [PubMed] [Google Scholar]
2. Emmons-Bell S, Johnson C, Roth G. Prevalence, incidence and survival of heart failure: a systematic review. Heart 2022; 108: 1351–1360. [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Roger VL. Epidemiology of heart failure. Circ Res 2013; 113: 646–659. [DOI] [PMC free article] [PubMed] [Google Scholar]
4. McMurray JJ, Adamopoulos S, Anker SD, et al. ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012. Rev Port Cardiol (Engl Ed) 2013; 7: 641–642. [Google Scholar]
5. Hoseinbor M, Vakhshoori M, Babak A, et al. Frequency of readmission in Iranian heart failure patients within six months after discharge and its association with guideline directed medical treatment. Drug Invent Today 2019; 11: 2737–2741. [Google Scholar]
6. Amirpour A, Vakhshoori M, Zavar R, et al. The effect of 3-month growth hormone administration and 12-month follow-up duration among heart failure patients four weeks after myocardial infarction: a randomized double-blinded clinical trial. Cardiovasc Therap 2021;2021; 2680107. [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Hobbs FR, Roalfe AK, Davis RC, et al. Prognosis of all-cause heart failure and borderline left ventricular systolic dysfunction: 5 year mortality follow-up of the Echocardiographic Heart of England Screening Study (ECHOES). Eur Heart J 2007; 28: 1128–1134. [DOI] [PubMed] [Google Scholar]
8. Bleumink GS, Knetsch AM, Sturkenboom MC, et al. Quantifying the heart failure epidemic: prevalence, incidence rate, lifetime risk and prognosis of heart failure: the Rotterdam Study. Eur Heart J 2004; 25: 1614–1619. [DOI] [PubMed] [Google Scholar]
9. Sahle BW, Owen AJ, Mutowo MP, et al. Prevalence of heart failure in Australia: a systematic review. BMC Cardiovasc Disord 2016; 16: 32. [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Ponikowski P, Anker SD, AlHabib KF, et al. Heart failure: preventing disease and death worldwide. ESC Heart Fail 2014; 1: 4–25. [DOI] [PubMed] [Google Scholar]
11. Cook C, Cole G, Asaria P, et al. The annual global economic burden of heart failure. Int J Cardiol 2014; 171: 368–376. [DOI] [PubMed] [Google Scholar]
12. Passantino A, Monitillo F, Iacoviello M, et al. Predicting mortality in patients with acute heart failure: role of risk scores. World J Cardiol 2015; 7: 902–911. [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Koosha P, Roohafza H, Sarrafzadegan N, et al. High sensitivity C-reactive protein predictive value for cardiovascular disease: a nested case control from Isfahan Cohort Study (ICS). Glob Heart 2020; 15: 3. [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Farsani AY, Vakhshoori M, Mansouri A, et al. Relation between hemoconcentration status and readmission plus mortality rate among Iranian individuals with decompensated heart failure. Int J Prev Med 2020; 11: 163. [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Bondariyan N, Vakhshoori M, Sadeghpour N, et al. Prognostic value of shock index, modified shock index, and age-adjusted derivatives in prediction of in-hospital mortality in patients with acute decompensated heart failure: Persian Registry of Cardiovascular Disease/Heart Failure Study. Anatol J Cardiol 2022; 26: 210–217. [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Heidarpour M, Sourani Z, Vakhshoori M, et al. Prognostic utility of shock index and modified shock index on long-term mortality in acute decompensated heart failure; Persian Registry of cardioVascular diseasE/Heart Failure (PROVE/HF) study. Acta Cardiol 2023; 78: 217–226. [DOI] [PubMed] [Google Scholar]
17. Ross R. Atherosclerosis – an inflammatory disease. N Engl J Med 1999; 340: 115–126. [DOI] [PubMed] [Google Scholar]
18. Libby P, Ridker P, Hansson G, et al. Transatlantic network on atherothrombosis. Inflammation in atherosclerosis: from pathophysiology to practice. J Am Coll Cardiol 2009; 54: 2129–2138. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Vakhshoori M, Nemati S, Sabouhi S, et al. Neutrophil to lymphocyte ratio (NLR) prognostic effects on heart failure; a systematic review and meta-analysis. BMC Cardiovasc Disord 2023; 23: 555. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Vakhshoori M, Nemati S, Sabouhi S, et al. Prognostic impact of monocyte-to-lymphocyte ratio in coronary heart disease: a systematic review and meta-analysis. J Int Med Res 2023; 51: 3000605231204469. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Vakhshoori M, Nemati S, Sabouhi S, et al. Selection of monocyte-to-lymphocyte ratio (MLR) or lymphocyte-to-monocyte ratio (LMR) as best prognostic tool in heart failure: a systematic review. SN Compr Clin Med 2023; 5: 227. [Google Scholar]
22. Glezeva N, Gilmer JF, Watson CJ, et al. A central role for monocyte–platelet interactions in heart failure. J Cardiovasc Pharmacol Ther 2016; 21: 245–261. [DOI] [PubMed] [Google Scholar]
23. Templeton AJ, Ace O, McNamara MG, et al. Prognostic role of platelet to lymphocyte ratio in solid tumors: a systematic review and meta-analysis. Cancer Epidemiol Biomarkers Prev 2014; 23: 1204–1212. [DOI] [PubMed] [Google Scholar]
24. Balta S, Ozturk C. The platelet–lymphocyte ratio: a simple, inexpensive and rapid prognostic marker for cardiovascular events. Platelets 2015; 26: 680–681. [DOI] [PubMed] [Google Scholar]
25. Zheng C-F, Liu W-Y, Zeng F-F, et al. Prognostic value of platelet-to-lymphocyte ratios among critically ill patients with acute kidney injury. Crit Care 2017; 21: 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Heidarpour M, Bashiri S, Vakhshoori M, et al. The association between platelet-to-lymphocyte ratio with mortality among patients suffering from acute decompensated heart failure. BMC Cardiovasc Disord 2021; 21: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Delcea C, Buzea CA, Vijan A, et al. Comparative role of hematological indices for the assessment of in-hospital outcome of heart failure patients. Scand Cardiovasc J 2021; 55: 227–2236. [DOI] [PubMed] [Google Scholar]
28. Dahlen B, Schulz A, Göbel S, et al. The impact of platelet indices on clinical outcome in heart failure: results from the MyoVasc study. ESC Heart Fail 2021; 8: 2991–3001. [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Stewart LA, Clarke M, Rovers M, et al. Preferred reporting items for a systematic review and meta-analysis of individual participant data: the PRISMA-IPD statement. JAMA 2015; 313: 1657–1665. [DOI] [PubMed] [Google Scholar]
30. Downes MJ, Brennan ML, Williams HC, et al. Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS). BMJ Open 2016; 6: e011458. [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Shea BJ, Grimshaw JM, Wells GA, et al. Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol 2007; 7: 1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Wan X, Wang W, Liu J, et al. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 2014; 14: 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Hozo SP, Djulbegovic B, Hozo I. Estimating the mean and variance from the median, range, and the size of a sample. BMC Med Res Methodol 2005; 5: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Ye G-l, Chen Q, Chen X, et al. The prognostic role of platelet-to-lymphocyte ratio in patients with acute heart failure: a cohort study. Sci Rep 2019; 9: 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Wu C-C, Wu C-H, Lee C-H, et al. Association between neutrophil percentage-to-albumin ratio (NPAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and long-term mortality in community-dwelling adults with heart failure: evidence from US NHANES 2005–2016. BMC Cardiovasc Disord 2023; 23: 312. [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Tamaki S, Nagai Y, Shutta R, et al. Combination of neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as a novel predictor of cardiac death in patients with acute decompensated heart failure with preserved left ventricular ejection fraction: a multicenter study. J Am Heart Assoc 2023; 12: e026326. [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Çakır MO. The prognostic significance of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio for long-term survival in patients with severe left ventricular dysfunction and implantable cardioverter defibrillator. Cureus 2023; 15: e47441. [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Liu Z, Zhang R, Xv Y, et al. A novel nomogram integrated with systemic inflammation markers and traditional prognostic factors for adverse events’ prediction in patients with chronic heart failure in the southwest of China. J Inflamm Res 2022: 15; 6785–6800. [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Kocaoglu S, Alatli T. The efficiency of the HALP score and the modified HALP score in predicting mortality in patients with acute heart failure presenting to the emergency department. J Coll Physicians Surg Pak 2022; 32: 706–711. [DOI] [PubMed] [Google Scholar]
40. Davran GB, Davran AÇ, Karabag T. The relationship of prognostic nutritional index with prognosis and inflammation in patient with heart failure and reduced ejection fraction. Nutr Health 2023; 29: 737–743. [DOI] [PubMed] [Google Scholar]
41. Arfsten H, Cho A, Prausmüller S, et al. Inflammation-based scores as a common tool for prognostic assessment in heart failure or cancer. Front Cardiovasc Med 2021; 8: 725903. [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Sadeghi MT, Esgandarian I, Nouri-Vaskeh M, et al. Role of circulatory leukocyte based indices in short-term mortality of patients with heart failure with reduced ejection fraction. Med Pharm Rep 2020; 93: 351. [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Turcato G, Sanchis-Gomar F, Cervellin G, et al. Evaluation of neutrophil–lymphocyte and platelet–lymphocyte ratios as predictors of 30-day mortality in patients hospitalized for an episode of acute decompensated heart failure. J Med Biochem 2019; 38: 452. [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Siedlecki Ł, Szyguła-Jurkiewicz B, Szczurek W, et al. Mortality risk factors in patients with advanced heart failure and diabetes mellitus. Kardiol Pol 2019; 77: 604–609. [DOI] [PubMed] [Google Scholar]
45. Yurtdaş M, Özdemir M, Aladağ N. Investigation of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and mean platelet volume in patients with compensated heart failure. J Acad Res Med 2018; 8: 67–71. [Google Scholar]
46. Demir M, Duyuler PT, Guray U, et al. Platelet to lymphocyte ratio on admission and prognosis in patients with acute cardiogenic pulmonary edema. J Emerg Med 2018; 55: 465–471. [DOI] [PubMed] [Google Scholar]
47. Szyguła-Jurkiewicz B, Siedlecki Ł, Pyka Ł, et al. Red blood cell distribution width, relative lymphocyte count, and type 2 diabetes predict all-cause mortality in patients with advanced heart failure. Pol Arch Intern Med 2018; 128: 115–120. [DOI] [PubMed] [Google Scholar]
48. Pourafkari L, Wang CK, Tajlil A, et al. Platelet–lymphocyte ratio in prediction of outcome of acute heart failure. Biomark Med 2018; 12: 63–70. [DOI] [PubMed] [Google Scholar]
49. Huang W-M, Cheng H-M, Huang C-J, et al. Hemographic indices are associated with mortality in acute heart failure. Sci Rep 2017; 7: 1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
50. Siniorakis EE, Tsarouhas K, Tsitsimpikou C, et al. Neutrophil to lymphocyte (N/L) and platelet to lymphocyte (P/L) ratios in differentiating acute heart failure from respiratory infection. Curr Vasc Pharmacol 2017; 15: 477–481. [DOI] [PubMed] [Google Scholar]
51. Durmus E, Kivrak T, Gerin F, et al. Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio are predictors of heart failure. Arq Bras Cardiol 2015; 105: 606–613. [DOI] [PMC free article] [PubMed] [Google Scholar]
52. Sabbah HN. Pathophysiology of acute heart failure syndrome: a knowledge gap. Heart Fail Rev 2017; 22: 621–639. [DOI] [PubMed] [Google Scholar]
53. Afari ME, Bhat T. Neutrophil to lymphocyte ratio (NLR) and cardiovascular diseases: an update. Expert Rev Cardiovasc Ther 2016; 14: 573–577. [DOI] [PubMed] [Google Scholar]
54. Reichlin T, Socrates T, Egli P, et al. Use of myeloperoxidase for risk stratification in acute heart failure. Clin Chem 2010; 56: 944–951. [DOI] [PubMed] [Google Scholar]
55. Baldus S, Heeschen C, Meinertz T, et al. Myeloperoxidase serum levels predict risk in patients with acute coronary syndromes. Circulation 2003; 108: 1440–1445. [DOI] [PubMed] [Google Scholar]
56. Sato Y, Takatsu Y, Kataoka K, et al. Serial circulating concentrations of C-reactive protein, interleukin (IL)-4, and IL-6 in patients with acute left heart decompensation. Clin Cardiol 1999; 22: 811–813. [DOI] [PMC free article] [PubMed] [Google Scholar]
57. Henderson RB, Hobbs JA, Mathies M, et al. Rapid recruitment of inflammatory monocytes is independent of neutrophil migration. Blood 2003; 102: 328–335. [DOI] [PubMed] [Google Scholar]
58. Meijers WC, de Boer RA. Common risk factors for heart failure and cancer. Cardiovasc Res 2019; 115: 844–853. [DOI] [PMC free article] [PubMed] [Google Scholar]
59. Gawaz M, Langer H, May AE. Platelets in inflammation and atherogenesis. J Clin Invest 2005; 115: 3378–3384. [DOI] [PMC free article] [PubMed] [Google Scholar]
60. Ginsberg M, Kozin F, O’Malley M, et al. Release of platelet constituents by monosodium urate crystals. J Clin Invest 1977; 60: 999–1007. [DOI] [PMC free article] [PubMed] [Google Scholar]
61. Getawa S, Bayleyegn B. Platelet, neutrophil and lymphocyte quantitative abnormalities in patients with heart failure: a retrospective study. Vasc Health Risk Manag 2023; 19: 69–78. [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Mojadidi MK, Galeas JN, Goodman-Meza D, et al. Thrombocytopaenia as a prognostic indicator in heart failure with reduced ejection fraction. Heart Lung Circ 2016; 25: 568–575. [DOI] [PubMed] [Google Scholar]
63. Gregg D, Goldschmidt-Clermont PJ. Platelets and cardiovascular disease. Circulation 2003; 108: e88–e90. [DOI] [PubMed] [Google Scholar]
64. Frangogiannis NG, Smith CW, Entman ML. The inflammatory response in myocardial infarction. Cardiovasc Res 2002; 53: 31–47. [DOI] [PubMed] [Google Scholar]
65. Rudiger A, Burckhardt OA, Harpes P, et al. The relative lymphocyte count on hospital admission is a risk factor for long-term mortality in patients with acute heart failure. Am J Emerg Med 2006; 24: 451–454. [DOI] [PubMed] [Google Scholar]
66. Hou M, Chen Y, Traverse JH, et al. ET-A receptor activity restrains coronary blood flow in the failing heart. J Cardiovasc Pharmacol 2004; 43: 764–769. [DOI] [PubMed] [Google Scholar]
67. Reina-Couto M, Pereira-Terra P, Quelhas-Santos J, et al. Inflammation in human heart failure: major mediators and therapeutic targets. Front Physiol 2021; 12: 746494. [DOI] [PMC free article] [PubMed] [Google Scholar]
68. Adamo L, Rocha-Resende C, Prabhu SD, et al. Reappraising the role of inflammation in heart failure. Nat Rev Cardiol 2020; 17: 269–285. [DOI] [PubMed] [Google Scholar]
69. Paulus WJ, Tschöpe C. A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. J Am Coll Cardiol 2013; 62: 263–271. [DOI] [PubMed] [Google Scholar]
70. Glezeva N, Baugh J. Role of inflammation in the pathogenesis of heart failure with preserved ejection fraction and its potential as a therapeutic target. Heart Fail Rev 2014; 19: 681–694. [DOI] [PubMed] [Google Scholar]
71. Tromp J, Westenbrink BD, Ouwerkerk W, et al. Identifying pathophysiological mechanisms in heart failure with reduced versus preserved ejection fraction. J Am Coll Cardiol 2018; 72: 1081–1090. [DOI] [PubMed] [Google Scholar]
72. Tromp J, Khan MA, Mentz RJ, et al. Biomarker profiles of acute heart failure patients with a mid-range ejection fraction. JACC Heart Fail 2017; 5: 507–517. [DOI] [PubMed] [Google Scholar]
73. Paulus WJ, Zile MR. From systemic inflammation to myocardial fibrosis: the heart failure with preserved ejection fraction paradigm revisited. Circ Res 2021; 128: 1451–1467. [DOI] [PMC free article] [PubMed] [Google Scholar]
74. Li H, Zhou Y, Ma Y, et al. The prognostic value of the platelet-to-lymphocyte ratio in acute coronary syndrome: a systematic review and meta-analysis. Kardiol Pol 2017; 75: 666–673. [DOI] [PubMed] [Google Scholar]
75. Kurtul A, Murat SN, Yarlioglues M, et al. Association of platelet-to-lymphocyte ratio with severity and complexity of coronary artery disease in patients with acute coronary syndromes. Am J Cardiol 2014; 114: 972–978. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

sj-doc-2-tak-10.1177_17539447241227287 – Supplemental material for The impact of platelet-to-lymphocyte ratio on clinical outcomes in heart failure: a systematic review and meta-analysis

Click here for additional data file.^{(64KB, doc)}

sj-docx-1-tak-10.1177_17539447241227287 – Supplemental material for The impact of platelet-to-lymphocyte ratio on clinical outcomes in heart failure: a systematic review and meta-analysis

Click here for additional data file.^{(374.7KB, docx)}

[bibr1-17539447241227287] 1. Bozkurt B, Coats AJS, Tsutsui H, et al. Universal definition and classification of heart failure: a report of the Heart Failure Society of America, Heart Failure Association of the European Society of cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition of Heart Failure: Endorsed by the Canadian Heart Failure Society, Heart Failure Association of India, Cardiac Society of Australia and New Zealand, and Chinese Heart Failure Association. Eur J Heart Fail 2021; 23: 352–380. [DOI] [PubMed] [Google Scholar]

[bibr2-17539447241227287] 2. Emmons-Bell S, Johnson C, Roth G. Prevalence, incidence and survival of heart failure: a systematic review. Heart 2022; 108: 1351–1360. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr3-17539447241227287] 3. Roger VL. Epidemiology of heart failure. Circ Res 2013; 113: 646–659. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr4-17539447241227287] 4. McMurray JJ, Adamopoulos S, Anker SD, et al. ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012. Rev Port Cardiol (Engl Ed) 2013; 7: 641–642. [Google Scholar]

[bibr5-17539447241227287] 5. Hoseinbor M, Vakhshoori M, Babak A, et al. Frequency of readmission in Iranian heart failure patients within six months after discharge and its association with guideline directed medical treatment. Drug Invent Today 2019; 11: 2737–2741. [Google Scholar]

[bibr6-17539447241227287] 6. Amirpour A, Vakhshoori M, Zavar R, et al. The effect of 3-month growth hormone administration and 12-month follow-up duration among heart failure patients four weeks after myocardial infarction: a randomized double-blinded clinical trial. Cardiovasc Therap 2021;2021; 2680107. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr7-17539447241227287] 7. Hobbs FR, Roalfe AK, Davis RC, et al. Prognosis of all-cause heart failure and borderline left ventricular systolic dysfunction: 5 year mortality follow-up of the Echocardiographic Heart of England Screening Study (ECHOES). Eur Heart J 2007; 28: 1128–1134. [DOI] [PubMed] [Google Scholar]

[bibr8-17539447241227287] 8. Bleumink GS, Knetsch AM, Sturkenboom MC, et al. Quantifying the heart failure epidemic: prevalence, incidence rate, lifetime risk and prognosis of heart failure: the Rotterdam Study. Eur Heart J 2004; 25: 1614–1619. [DOI] [PubMed] [Google Scholar]

[bibr9-17539447241227287] 9. Sahle BW, Owen AJ, Mutowo MP, et al. Prevalence of heart failure in Australia: a systematic review. BMC Cardiovasc Disord 2016; 16: 32. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr10-17539447241227287] 10. Ponikowski P, Anker SD, AlHabib KF, et al. Heart failure: preventing disease and death worldwide. ESC Heart Fail 2014; 1: 4–25. [DOI] [PubMed] [Google Scholar]

[bibr11-17539447241227287] 11. Cook C, Cole G, Asaria P, et al. The annual global economic burden of heart failure. Int J Cardiol 2014; 171: 368–376. [DOI] [PubMed] [Google Scholar]

[bibr12-17539447241227287] 12. Passantino A, Monitillo F, Iacoviello M, et al. Predicting mortality in patients with acute heart failure: role of risk scores. World J Cardiol 2015; 7: 902–911. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr13-17539447241227287] 13. Koosha P, Roohafza H, Sarrafzadegan N, et al. High sensitivity C-reactive protein predictive value for cardiovascular disease: a nested case control from Isfahan Cohort Study (ICS). Glob Heart 2020; 15: 3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr14-17539447241227287] 14. Farsani AY, Vakhshoori M, Mansouri A, et al. Relation between hemoconcentration status and readmission plus mortality rate among Iranian individuals with decompensated heart failure. Int J Prev Med 2020; 11: 163. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr15-17539447241227287] 15. Bondariyan N, Vakhshoori M, Sadeghpour N, et al. Prognostic value of shock index, modified shock index, and age-adjusted derivatives in prediction of in-hospital mortality in patients with acute decompensated heart failure: Persian Registry of Cardiovascular Disease/Heart Failure Study. Anatol J Cardiol 2022; 26: 210–217. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr16-17539447241227287] 16. Heidarpour M, Sourani Z, Vakhshoori M, et al. Prognostic utility of shock index and modified shock index on long-term mortality in acute decompensated heart failure; Persian Registry of cardioVascular diseasE/Heart Failure (PROVE/HF) study. Acta Cardiol 2023; 78: 217–226. [DOI] [PubMed] [Google Scholar]

[bibr17-17539447241227287] 17. Ross R. Atherosclerosis – an inflammatory disease. N Engl J Med 1999; 340: 115–126. [DOI] [PubMed] [Google Scholar]

[bibr18-17539447241227287] 18. Libby P, Ridker P, Hansson G, et al. Transatlantic network on atherothrombosis. Inflammation in atherosclerosis: from pathophysiology to practice. J Am Coll Cardiol 2009; 54: 2129–2138. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr19-17539447241227287] 19. Vakhshoori M, Nemati S, Sabouhi S, et al. Neutrophil to lymphocyte ratio (NLR) prognostic effects on heart failure; a systematic review and meta-analysis. BMC Cardiovasc Disord 2023; 23: 555. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr20-17539447241227287] 20. Vakhshoori M, Nemati S, Sabouhi S, et al. Prognostic impact of monocyte-to-lymphocyte ratio in coronary heart disease: a systematic review and meta-analysis. J Int Med Res 2023; 51: 3000605231204469. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr21-17539447241227287] 21. Vakhshoori M, Nemati S, Sabouhi S, et al. Selection of monocyte-to-lymphocyte ratio (MLR) or lymphocyte-to-monocyte ratio (LMR) as best prognostic tool in heart failure: a systematic review. SN Compr Clin Med 2023; 5: 227. [Google Scholar]

[bibr22-17539447241227287] 22. Glezeva N, Gilmer JF, Watson CJ, et al. A central role for monocyte–platelet interactions in heart failure. J Cardiovasc Pharmacol Ther 2016; 21: 245–261. [DOI] [PubMed] [Google Scholar]

[bibr23-17539447241227287] 23. Templeton AJ, Ace O, McNamara MG, et al. Prognostic role of platelet to lymphocyte ratio in solid tumors: a systematic review and meta-analysis. Cancer Epidemiol Biomarkers Prev 2014; 23: 1204–1212. [DOI] [PubMed] [Google Scholar]

[bibr24-17539447241227287] 24. Balta S, Ozturk C. The platelet–lymphocyte ratio: a simple, inexpensive and rapid prognostic marker for cardiovascular events. Platelets 2015; 26: 680–681. [DOI] [PubMed] [Google Scholar]

[bibr25-17539447241227287] 25. Zheng C-F, Liu W-Y, Zeng F-F, et al. Prognostic value of platelet-to-lymphocyte ratios among critically ill patients with acute kidney injury. Crit Care 2017; 21: 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr26-17539447241227287] 26. Heidarpour M, Bashiri S, Vakhshoori M, et al. The association between platelet-to-lymphocyte ratio with mortality among patients suffering from acute decompensated heart failure. BMC Cardiovasc Disord 2021; 21: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr27-17539447241227287] 27. Delcea C, Buzea CA, Vijan A, et al. Comparative role of hematological indices for the assessment of in-hospital outcome of heart failure patients. Scand Cardiovasc J 2021; 55: 227–2236. [DOI] [PubMed] [Google Scholar]

[bibr28-17539447241227287] 28. Dahlen B, Schulz A, Göbel S, et al. The impact of platelet indices on clinical outcome in heart failure: results from the MyoVasc study. ESC Heart Fail 2021; 8: 2991–3001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr29-17539447241227287] 29. Stewart LA, Clarke M, Rovers M, et al. Preferred reporting items for a systematic review and meta-analysis of individual participant data: the PRISMA-IPD statement. JAMA 2015; 313: 1657–1665. [DOI] [PubMed] [Google Scholar]

[bibr30-17539447241227287] 30. Downes MJ, Brennan ML, Williams HC, et al. Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS). BMJ Open 2016; 6: e011458. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr31-17539447241227287] 31. Shea BJ, Grimshaw JM, Wells GA, et al. Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol 2007; 7: 1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr32-17539447241227287] 32. Wan X, Wang W, Liu J, et al. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 2014; 14: 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr33-17539447241227287] 33. Hozo SP, Djulbegovic B, Hozo I. Estimating the mean and variance from the median, range, and the size of a sample. BMC Med Res Methodol 2005; 5: 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr34-17539447241227287] 34. Ye G-l, Chen Q, Chen X, et al. The prognostic role of platelet-to-lymphocyte ratio in patients with acute heart failure: a cohort study. Sci Rep 2019; 9: 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr35-17539447241227287] 35. Wu C-C, Wu C-H, Lee C-H, et al. Association between neutrophil percentage-to-albumin ratio (NPAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and long-term mortality in community-dwelling adults with heart failure: evidence from US NHANES 2005–2016. BMC Cardiovasc Disord 2023; 23: 312. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr36-17539447241227287] 36. Tamaki S, Nagai Y, Shutta R, et al. Combination of neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as a novel predictor of cardiac death in patients with acute decompensated heart failure with preserved left ventricular ejection fraction: a multicenter study. J Am Heart Assoc 2023; 12: e026326. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr37-17539447241227287] 37. Çakır MO. The prognostic significance of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio for long-term survival in patients with severe left ventricular dysfunction and implantable cardioverter defibrillator. Cureus 2023; 15: e47441. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr38-17539447241227287] 38. Liu Z, Zhang R, Xv Y, et al. A novel nomogram integrated with systemic inflammation markers and traditional prognostic factors for adverse events’ prediction in patients with chronic heart failure in the southwest of China. J Inflamm Res 2022: 15; 6785–6800. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr39-17539447241227287] 39. Kocaoglu S, Alatli T. The efficiency of the HALP score and the modified HALP score in predicting mortality in patients with acute heart failure presenting to the emergency department. J Coll Physicians Surg Pak 2022; 32: 706–711. [DOI] [PubMed] [Google Scholar]

[bibr40-17539447241227287] 40. Davran GB, Davran AÇ, Karabag T. The relationship of prognostic nutritional index with prognosis and inflammation in patient with heart failure and reduced ejection fraction. Nutr Health 2023; 29: 737–743. [DOI] [PubMed] [Google Scholar]

[bibr41-17539447241227287] 41. Arfsten H, Cho A, Prausmüller S, et al. Inflammation-based scores as a common tool for prognostic assessment in heart failure or cancer. Front Cardiovasc Med 2021; 8: 725903. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr42-17539447241227287] 42. Sadeghi MT, Esgandarian I, Nouri-Vaskeh M, et al. Role of circulatory leukocyte based indices in short-term mortality of patients with heart failure with reduced ejection fraction. Med Pharm Rep 2020; 93: 351. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr43-17539447241227287] 43. Turcato G, Sanchis-Gomar F, Cervellin G, et al. Evaluation of neutrophil–lymphocyte and platelet–lymphocyte ratios as predictors of 30-day mortality in patients hospitalized for an episode of acute decompensated heart failure. J Med Biochem 2019; 38: 452. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr44-17539447241227287] 44. Siedlecki Ł, Szyguła-Jurkiewicz B, Szczurek W, et al. Mortality risk factors in patients with advanced heart failure and diabetes mellitus. Kardiol Pol 2019; 77: 604–609. [DOI] [PubMed] [Google Scholar]

[bibr45-17539447241227287] 45. Yurtdaş M, Özdemir M, Aladağ N. Investigation of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and mean platelet volume in patients with compensated heart failure. J Acad Res Med 2018; 8: 67–71. [Google Scholar]

[bibr46-17539447241227287] 46. Demir M, Duyuler PT, Guray U, et al. Platelet to lymphocyte ratio on admission and prognosis in patients with acute cardiogenic pulmonary edema. J Emerg Med 2018; 55: 465–471. [DOI] [PubMed] [Google Scholar]

[bibr47-17539447241227287] 47. Szyguła-Jurkiewicz B, Siedlecki Ł, Pyka Ł, et al. Red blood cell distribution width, relative lymphocyte count, and type 2 diabetes predict all-cause mortality in patients with advanced heart failure. Pol Arch Intern Med 2018; 128: 115–120. [DOI] [PubMed] [Google Scholar]

[bibr48-17539447241227287] 48. Pourafkari L, Wang CK, Tajlil A, et al. Platelet–lymphocyte ratio in prediction of outcome of acute heart failure. Biomark Med 2018; 12: 63–70. [DOI] [PubMed] [Google Scholar]

[bibr49-17539447241227287] 49. Huang W-M, Cheng H-M, Huang C-J, et al. Hemographic indices are associated with mortality in acute heart failure. Sci Rep 2017; 7: 1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr50-17539447241227287] 50. Siniorakis EE, Tsarouhas K, Tsitsimpikou C, et al. Neutrophil to lymphocyte (N/L) and platelet to lymphocyte (P/L) ratios in differentiating acute heart failure from respiratory infection. Curr Vasc Pharmacol 2017; 15: 477–481. [DOI] [PubMed] [Google Scholar]

[bibr51-17539447241227287] 51. Durmus E, Kivrak T, Gerin F, et al. Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio are predictors of heart failure. Arq Bras Cardiol 2015; 105: 606–613. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr52-17539447241227287] 52. Sabbah HN. Pathophysiology of acute heart failure syndrome: a knowledge gap. Heart Fail Rev 2017; 22: 621–639. [DOI] [PubMed] [Google Scholar]

[bibr53-17539447241227287] 53. Afari ME, Bhat T. Neutrophil to lymphocyte ratio (NLR) and cardiovascular diseases: an update. Expert Rev Cardiovasc Ther 2016; 14: 573–577. [DOI] [PubMed] [Google Scholar]

[bibr54-17539447241227287] 54. Reichlin T, Socrates T, Egli P, et al. Use of myeloperoxidase for risk stratification in acute heart failure. Clin Chem 2010; 56: 944–951. [DOI] [PubMed] [Google Scholar]

[bibr55-17539447241227287] 55. Baldus S, Heeschen C, Meinertz T, et al. Myeloperoxidase serum levels predict risk in patients with acute coronary syndromes. Circulation 2003; 108: 1440–1445. [DOI] [PubMed] [Google Scholar]

[bibr56-17539447241227287] 56. Sato Y, Takatsu Y, Kataoka K, et al. Serial circulating concentrations of C-reactive protein, interleukin (IL)-4, and IL-6 in patients with acute left heart decompensation. Clin Cardiol 1999; 22: 811–813. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr57-17539447241227287] 57. Henderson RB, Hobbs JA, Mathies M, et al. Rapid recruitment of inflammatory monocytes is independent of neutrophil migration. Blood 2003; 102: 328–335. [DOI] [PubMed] [Google Scholar]

[bibr58-17539447241227287] 58. Meijers WC, de Boer RA. Common risk factors for heart failure and cancer. Cardiovasc Res 2019; 115: 844–853. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr59-17539447241227287] 59. Gawaz M, Langer H, May AE. Platelets in inflammation and atherogenesis. J Clin Invest 2005; 115: 3378–3384. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr60-17539447241227287] 60. Ginsberg M, Kozin F, O’Malley M, et al. Release of platelet constituents by monosodium urate crystals. J Clin Invest 1977; 60: 999–1007. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr61-17539447241227287] 61. Getawa S, Bayleyegn B. Platelet, neutrophil and lymphocyte quantitative abnormalities in patients with heart failure: a retrospective study. Vasc Health Risk Manag 2023; 19: 69–78. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr62-17539447241227287] 62. Mojadidi MK, Galeas JN, Goodman-Meza D, et al. Thrombocytopaenia as a prognostic indicator in heart failure with reduced ejection fraction. Heart Lung Circ 2016; 25: 568–575. [DOI] [PubMed] [Google Scholar]

[bibr63-17539447241227287] 63. Gregg D, Goldschmidt-Clermont PJ. Platelets and cardiovascular disease. Circulation 2003; 108: e88–e90. [DOI] [PubMed] [Google Scholar]

[bibr64-17539447241227287] 64. Frangogiannis NG, Smith CW, Entman ML. The inflammatory response in myocardial infarction. Cardiovasc Res 2002; 53: 31–47. [DOI] [PubMed] [Google Scholar]

[bibr65-17539447241227287] 65. Rudiger A, Burckhardt OA, Harpes P, et al. The relative lymphocyte count on hospital admission is a risk factor for long-term mortality in patients with acute heart failure. Am J Emerg Med 2006; 24: 451–454. [DOI] [PubMed] [Google Scholar]

[bibr66-17539447241227287] 66. Hou M, Chen Y, Traverse JH, et al. ET-A receptor activity restrains coronary blood flow in the failing heart. J Cardiovasc Pharmacol 2004; 43: 764–769. [DOI] [PubMed] [Google Scholar]

[bibr67-17539447241227287] 67. Reina-Couto M, Pereira-Terra P, Quelhas-Santos J, et al. Inflammation in human heart failure: major mediators and therapeutic targets. Front Physiol 2021; 12: 746494. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr68-17539447241227287] 68. Adamo L, Rocha-Resende C, Prabhu SD, et al. Reappraising the role of inflammation in heart failure. Nat Rev Cardiol 2020; 17: 269–285. [DOI] [PubMed] [Google Scholar]

[bibr69-17539447241227287] 69. Paulus WJ, Tschöpe C. A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. J Am Coll Cardiol 2013; 62: 263–271. [DOI] [PubMed] [Google Scholar]

[bibr70-17539447241227287] 70. Glezeva N, Baugh J. Role of inflammation in the pathogenesis of heart failure with preserved ejection fraction and its potential as a therapeutic target. Heart Fail Rev 2014; 19: 681–694. [DOI] [PubMed] [Google Scholar]

[bibr71-17539447241227287] 71. Tromp J, Westenbrink BD, Ouwerkerk W, et al. Identifying pathophysiological mechanisms in heart failure with reduced versus preserved ejection fraction. J Am Coll Cardiol 2018; 72: 1081–1090. [DOI] [PubMed] [Google Scholar]

[bibr72-17539447241227287] 72. Tromp J, Khan MA, Mentz RJ, et al. Biomarker profiles of acute heart failure patients with a mid-range ejection fraction. JACC Heart Fail 2017; 5: 507–517. [DOI] [PubMed] [Google Scholar]

[bibr73-17539447241227287] 73. Paulus WJ, Zile MR. From systemic inflammation to myocardial fibrosis: the heart failure with preserved ejection fraction paradigm revisited. Circ Res 2021; 128: 1451–1467. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr74-17539447241227287] 74. Li H, Zhou Y, Ma Y, et al. The prognostic value of the platelet-to-lymphocyte ratio in acute coronary syndrome: a systematic review and meta-analysis. Kardiol Pol 2017; 75: 666–673. [DOI] [PubMed] [Google Scholar]

[bibr75-17539447241227287] 75. Kurtul A, Murat SN, Yarlioglues M, et al. Association of platelet-to-lymphocyte ratio with severity and complexity of coronary artery disease in patients with acute coronary syndromes. Am J Cardiol 2014; 114: 972–978. [DOI] [PubMed] [Google Scholar]

PERMALINK

The impact of platelet-to-lymphocyte ratio on clinical outcomes in heart failure: a systematic review and meta-analysis

Mehrbod Vakhshoori

Niloofar Bondariyan

Sadeq Sabouhi

Keivan Kiani

Nazanin Alaei Faradonbeh

Sayed Ali Emami

Mehrnaz Shakarami

Farbod Khanizadeh

Shahin Sanaei

Niloofaralsadat Motamedi

Davood Shafie

Roles

Abstract

Background:

Methods:

Results:

Conclusion:

Plain language summary

Introduction

Materials and methods

Protocol registration

Eligibility criteria

Search strategy

Data management and selection process

Figure 1.

Data collection process

Risk of bias and quality assessment

Statistical analysis

Results

Table 1.

Figure 2.

Table 2.

PLR and mortality

Figure 3.

Figure 4.

Figure 5.

PLR based on survival and death subgroups

Figure 6.

Figure 7.

PLR and mortality HR

Figure 8.

Figure 9.

Figure 10.

PLR and rehospitalization

PLR and HF detection

PLR and HF worsening

PLR quartiles and HF outcomes

PLR tertiles and HF outcomes

PLR cutoff and HF outcomes

PLR and HF status

Figure 11.

Figure 12.

Discussion

Supplemental Material

Acknowledgments

Footnotes

Contributor Information

Declarations

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases